CERES :: Browsing by Author "Auger, Daniel J."

Browsing by Author "Auger, Daniel J."

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Open Access
Accuracy versus simplicity in online battery model identification
(IEEE, 2016-09-22) Fotouhi, Abbas; Auger, Daniel J.; Propp, Karsten; Longo, Stefano
This paper presents a framework for battery modeling in online, real-time applications where accuracy is important but speed is the key. The framework allows users to select model structures with the smallest number of parameters that is consistent with the accuracy requirements of the target application. The tradeoff between accuracy and speed in a battery model identification process is explored using different model structures and parameter-fitting algorithms. Pareto optimal sets are obtained, allowing a designer to select an appropriate compromise between accuracy and speed. In order to get a clearer understanding of the battery model identification problem, “identification surfaces” are presented. As an outcome of the battery identification surfaces, a new analytical solution is derived for battery model identification using a closed-form formula to obtain a battery’s ohmic resistance and open circuit voltage from measurement data. This analytical solution is used as a benchmark for comparison of other fitting algorithms and it is also used in its own right in a practical scenario for state-of-charge estimation. A simulation study is performed to demonstrate the effectiveness of the proposed framework and the simulation results are verified by conducting experimental tests on a small NiMH battery pack.
Open Access
Advanced state of charge estimation for lithium-sulfur batteries.
(2017-05) Propp, Karsten; Auger, Daniel J.
Lithium-sulfur (Li-S) batteries have a high theoretical energy density, which could outperform classic Li-ion technology in weight, manufacturing costs, safety and environmental impact. The aim of this study is to extend the research around Li-S through practical applications, specifically to develop a Li-S battery state of charge (SoC) estimation in the environment of electrical vehicles. This thesis is written in paper based form and is organised into three main areas. Part I introduces general topic of vehicle electrification, the framework of the research project REVB, mechanisms of Li-S cells and techniques for SoC estimation. The major scientific contribution is given in Part II within three studies in paper-based form. In Paper 1, a simple and fast running equivalent circuit network discharge model for Li-S cells over different temperature levels is presented. Paper 2 uses the model as an observer for Kalman filter (KF) based SoC estimation, employing and comparing the extended Kalman filter, the unscented Kalman filter and the Particle filter. Generally, a robust Li-S cell SoC estimator could be realized for realistic scenarios. To improve the robustness of the SoC estimation with different current densities, in Paper 3 a fast running online parameter identification method is applied, which could be used to improve the battery model as well as the SoC estimation precision. In Part III, the results are discussed and future directions are given to improve the SoC estimation accuracy for a wider range of applications and conditions. The final conclusion of this work is that a robust Li-S cell SoC estimation can be achieved with Kalman filter types of algorithms. Amongst the approaches of this study, the online parameter identification approach could deliver the best results and also contains most potential for further improvement.
Open Access
Analysis of autopilot disengagements occurring during autonomous vehicle testing
(IEEE, 2017-12-20) Lv, Chen; Cao, Dongpu; Zhao, Yifan; Auger, Daniel J.; Sullman, Mark; Wang, Huaji; Millen Dutka, Laura; Skrypchuk, Lee; Mouzakitis, Alexandros
In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle U+02BC s safety and ride comfort. In the U.S state of California, the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing. On 1 January 2016, seven manufacturers submitted their first disengagement reports: Bosch, Delphi, Google, Nissan, Mercedes-Benz, Volkswagen, and Tesla Motors. This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers U+0028 SAE U+0029 Level 2 and Level 3 automation technologies. Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail. The mechanisms and time taken for take-over transition occurred in disengagements are studied. Finally, recommendations for OEMs, manufacturers, and government organizations are also discussed.
Open Access
Application of advanced tree search and proximal policy optimization on formula-E race strategy development
(Elsevier, 2022-02-25) Liu, Xuze; Fotouhi, Abbas; Auger, Daniel J.
Energy and thermal management is a crucial element in Formula-E race strategy development. Most published literature focuses on the optimal management strategy for a single lap and results in sub-optimal solutions to the larger multi-lap problem. In this study, two Monte Carlo Tree Search (MCTS) enhancement techniques are proposed for multi-lap Formula-E racing strategy development. It is shown that using the bivariate Gaussian distribution enhancement, race finishing time improves by at least 0.25% and its variance reduces by more than 26%. Compared to the published conventional MCTS technique used in multi-lap problems, this proposed technique is proved to bring a remarkable enhancement with no additional computational time cost. By further enhancing the MCTS using proximal policy optimization, the final product is capable of generating more than 0.5% quicker race time solutions and improving the consistency by over 90% which makes it a very suitable method particularly when enough training time is guaranteed
Open Access
Battery temperature prediction using an adaptive neuro-fuzzy inference system
(MDPI, 2024-03-01) Zhang, Hanwen; Fotouhi, Abbas; Auger, Daniel J.; Lowe, Matt
Maintaining batteries within a specific temperature range is vital for safety and efficiency, as extreme temperatures can degrade a battery’s performance and lifespan. In addition, battery temperature is the key parameter in battery safety regulations. Battery thermal management systems (BTMSs) are pivotal in regulating battery temperature. While current BTMSs offer real-time temperature monitoring, their lack of predictive capability poses a limitation. This study introduces a novel hybrid system that combines a machine learning-based battery temperature prediction model with an online battery parameter identification unit. The identification unit continuously updates the battery’s electrical parameters in real time, enhancing the prediction model’s accuracy. The prediction model employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) and considers various input parameters, such as ambient temperature, the battery’s current temperature, internal resistance, and open-circuit voltage. The model accurately predicts the battery’s future temperature in a finite time horizon by dynamically adjusting thermal and electrical parameters based on real-time data. Experimental tests are conducted on Li-ion (NCA and LFP) cylindrical cells across a range of ambient temperatures to validate the system’s accuracy under varying conditions, including state of charge and a dynamic load current. The proposed models prioritise simplicity to ensure real-time industrial applicability.
Open Access
Characterization of driver neuromuscular dynamics for human-automation collaboration design of automated vehicles
(IEEE, 2018-03-05) Lv, Chen; Wang, Huaji; Cao, Dongpu; Zhao, Yifan; Auger, Daniel J.; Sullman, Mark; Matthias, Rebecca; Skrypchuk, Lee; Mouzakitis, Alexandros
In order to design an advanced human-automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper a dynamic model of drivers' neuromuscular interaction with a steering wheel is firstly established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver-steering-wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss-Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles.
Open Access
Charging characterization of a high‐capacity lithium‐sulfur pouch cell for state estimation–an experimental approach
(Wiley, 2022-09-16) Shateri, Neda; Auger, Daniel J.; Fotouhi, Abbas; Brighton, James
Lithium-Sulfur (Li-S) battery is a next-generation technology, which is promising for applications that require higher energy density in comparison to the available lithium-ion batteries. Along with the ongoing research on Li-S cell material development and manufacturing to improve this technology, engineers are also working on Li-S battery management systems (BMS). The existing BMS algorithms, which are developed for lithium-ion batteries, are not useable for the Li-S mainly due to its constant voltage plateau during the discharge phase. As a result, the Li-S system has poor observability during discharge, which limits the BMS functionality that can be implemented from discharge information alone, and it is worth considering if information from charging is useful. In this study, the charging behavior of a high-capacity pouch cell is investigated and characterized for the purpose of state estimation in a BMS. Several tests are conducted on prototype Li-S cells at different temperatures and age levels. An online feature extraction method is then used in combination with a classification technique to estimate the cell's states during charging. The proposed charging estimators can provide accurate initialization for state estimation accuracy during discharge by providing good estimates of the post-charging state of charge (ie, around 3%) and capacity after fading (ie, around 2%).
Open Access
Concurrent real-time estimation of state of health and maximum available power in lithium-sulfur batteries
(MDPI, 2018-08-16) Knap, Vaclav; Auger, Daniel J.; Propp, Karsten; Fotouhi, Abbas; Stroe, Daniel-Ioan
Lithium-sulfur (Li-S) batteries are an emerging energy storage technology with higher performance than lithium-ion batteries in terms of specific capacity and energy density. However, several scientific and technological gaps need to be filled before Li-S batteries will penetrate the market at a large scale. One such gap, which is tackled in this paper, is represented by the estimation of state-of-health (SOH). Li-S batteries exhibit a complex behaviour due to their inherent mechanisms, which requires a special tailoring of the already literature-available state-of-charge (SOC) and SOH estimation algorithms. In this work, a model of SOH based on capacity fade and power fade has been proposed and incorporated in a state estimator using dual extended Kalman filters has been used to simultaneously estimate Li-S SOC and SOH. The dual extended Kalman filter’s internal estimates of equivalent circuit network parameters have also been used to the estimate maximum available power of the battery at any specified instant. The proposed estimators have been successfully applied to both fresh and aged Li-S pouch cells, showing that they can accurately track accurately the battery SOC, SOH, and power, providing that initial conditions are suitable. However, the estimation of the Li-S battery cells’ capacity fade is shown to be more complex, because the practical available capacity varies highly with the applied current rates and the dynamics of the mission profile.
Open Access
Design, build and validation of a low-cost programmable battery cycler
(The Electrochemical Society, 2016-12-07) Propp, Karsten; Fotouhi, Abbas; Knap, Vaclav; Auger, Daniel J.
The availability of laboratory grade equipment for battery tests is usually limited due to high costs of the hardware. Especially for lithium-sulfur (Li-S) batteries these experiments can be time intensive since the cells need to be precycled and are usually cycled with relatively low loads. To improve the availability of test hardware, this paper conducts a study to design and test a low cost solution for cycling and testing batteries for tasks that do not necessarily need the high precision of professional hardware. While the described solution is in principle independent of the cell chemistry, here it is specifically optimized to fit to Li-S batteries. To evaluate the accuracy of the presented battery cycler, the hardware is tested and compared with a professional Kepco bipolar power source. The results indicate the usefulness for application oriented battery tests with real life cycles, although inaccuracies occur in the current measurements.
Open Access
Deterministic observability calculations for zero-dimensional models of lithium–sulfur batteries
(Elsevier, 2024-03-29) Rodriguez, Veronica M.; Shateri, Neda; Fotouhi, Abbas; Propp, Karsten; Auger, Daniel J.
Among the various energy storage technologies under development, the lithium‑sulfur (Li–S) battery has considerable promise due to its higher theoretical energy density, small environmental footprint, and low projected costs. One of the main challenges posed by Li–S is the need for a battery management system (BMS) that can accommodate the system's complex multi-step redox behaviours; conventional approaches for lithium-ion batteries do not transfer. Most existing approaches rely on equivalent circuit network models, but there is growing interest in ‘zero-dimensional’ electrochemical models which can potentially give insights into the relative polysulfide species concentrations present at any given time. To be useful for state estimation, a model must be ‘observable’: it must be possible to uniquely determine the internal state through observation of the system's behaviour over time. Previous studies have assessed observability using numerical methods, which is an approximation. This study derives an analytic expression for the observability criterion, which allows greater confidence in the results. The analytic observability criterion is then validated against a numerical comparator. A zero-dimensional model from the literature is translated into an ordinary differential equation (ODE) form to define the state variables matrix A, the output matrix C, and subsequently the observability matrix O. These are compared to simulated numerical equivalents. In addition, the sensitivity of the numerical process has been demonstrated. The results have the potential to offer greater confidence in conclusions around observability, which in turn gives greater confidence in the effects of any algorithms based on them.
Open Access
Development of a hybrid adaptive neuro-fuzzy inference system with coulomb-counting state-of-charge estimator for lithium–sulphur battery
(Springer, 2022-11-08) Valencia, Nicolas; Fotouhi, Abbas; Shateri, Neda; Auger, Daniel J.
This study presents the development of an improved state of charge (SOC) estimation technique for lithium–sulphur (Li–S) batteries. This is a promising technology with advantages in comparison with the existing lithium-ion (Li-ion) batteries such as lower production cost and higher energy density. In this study, a state-of-the-art Li–S prototype cell is subjected to experimental tests, which are carried out to replicate real-life duty cycles. A system identification technique is then used on the experimental test results to parameterize an equivalent circuit model for the Li–S cell. The identification results demonstrate unique features of the cell’s voltage-SOC and ohmic resistance-SOC curves, in which a large flat region is observed in the middle SOC range. Due to this, voltage and resistance parameters are not sufficient to accurately estimate SOC under various initial conditions. To solve this problem, a forgetting factor recursive least squares (FFRLS) identification technique is used, yielding four parameters which are then used to train an adaptive neuro-fuzzy inference system (ANFIS). The Sugeno-type fuzzy system features four inputs and one output (SOC), totalling 375 rules. Each of the inputs features Gaussian-type membership functions while the output is of a linear type. This network is then combined with the coulomb-counting method to obtain a hybrid estimator that can accurately estimate SOC for a Li–S cell under various conditions with a maximum error of 1.64%, which outperforms the existing methods of Li–S battery SOC estimation.
Open Access
Driver distraction detection using experimental methods and machine learning algorithms.
(Cranfield University, 2020-02) Zhang, Zhaozhong; Velenis, Efstathios; Fotouhi, Abbas; Auger, Daniel J.
Driver distraction causes numerous road accidents, which is approximately equal to 25% of the total crashes according to the reports by the National Highway Traffic Safety Administration. Warnings can be helpful to mitigate the risks caused by driver distraction. Previous studies on driver distraction detection have not sufficiently found relevant input features to filter insignificant information, thus limiting the improvement of efficiency. Moreover, the disadvantages of driving simulators and public roads pose a challenge in collecting suitable data for feature identification and comparisons of performance among driver distraction detection algorithms. While the previous research focuses on improving prediction accuracy, shortening the prediction time is critical in giving timely warnings to drivers. This thesis aims at detecting driver distraction, which could provide faster and accurate warnings to drivers. The developed method is implemented by cutting the redundancy and irrelevant information fed to the algorithms and instead selecting suitable algorithms that achieve the balance between the prediction accuracy and prediction time. Moreover, a closed testing field supplies an environment for collecting more accurate information to identify the relevant features and to determine suitable algorithms. In this study, open-source data and experimental data are used. The results show that a balance between the prediction accuracy and the prediction time is achieved by feeding the relevant features and using suitable machine learning algorithms (e.g. Decision Tree). Compared with existing state-of-the-art methods, the prediction accuracy of the method proposed in this study has reached approximately the same level. More importantly, the efficiency has improved, including reduced prediction time and fewer input features. Consequently, less computer storage is used.
Open Access
Driver distraction detection using machine learning algorithms – an experimental approach
(Inderscience, 2021-05-08) Zhang, Zhaozhong; Velenis, Efstathios; Fotouhi, Abbas; Auger, Daniel J.; Cao, Dongpu
Driver distraction is the leading cause of accidents that contributes to 25% of all road crashes. In order to reduce the risks posed by distraction, warning must be given to the driver once distraction is detected. According to the literature, no rankings of relevant features have been presented. In this study, the most relevant features in detecting driver distraction are identified in a closed testing environment. The relevant features are found to be the mean values of speed and lane deviation, maximum values of eye gaze in direction, and head movement in direction. After the relevant features have been identified, pre-processed data with relevant features are fed into decision tree classifiers to discriminate the data into normal and distracted driving. The results show that detection accuracy of 78.4% using decision tree can be achieved. By eliminating unhelpful features, the time required to process data is reduced by around 40% to make the proposed technique suitable for real-time application.
Open Access
Efficient mode transition control for DM-PHEV with mechanical hysteresis based on piecewise affine H∞ strategy
(IEEE, 2023-01-09) Liang, Cong; Xu, Xing; Auger, Daniel J.; Wang, Feng; Wang, Shaohua
A dual motor plug-in hybrid electric vehicle (DM-PHEVs) can achieve higher power and better fuel economy through Mode Transition Process (MTP) from pure electric to hybrid driving modes. In a DM-PHEV, the MTP is more complex, with more components to be managed. As well as being a combination of a discrete stage transition and a system with continuous state evolution, the several actuators exhibit significant discontinuous dynamics and different characteristics from each other, particularly mechanical hysteresis. This makes the design of a coordinated controller challenging. In this paper, a two-layer coordinated control strategy is proposed. The upper layer is based on a stage-dependent piecewise-affine (PWA) model which is used to develop a PWA-static output feedback H ∞ strategy (PWA-SOF). The lower layer is based on a simplified actuator lag model, and a H ∞ design technique is used to develop a robust torque controller that reduces the effect of mechanical hysteresis. The resulting strategy is described as a piecewise-affine modified static output feedback (PW-MSOF) algorithm. (While the individual elements are not novel contributions to control theory, the combination and application to this problem is.) Performance indices are defined and hardware-in-the-loop (HiL) test shows that the new controller can effectively suppress the vehicle jerk without adversely affecting other aspects of system behaviour.
Open Access
Electric vehicle battery model identification and state of charge estimation in real world driving cycles
(Institute of Electrical and Electronics Engineers, 2015-09-24) Fotouhi, Abbas; Propp, Karsten; Auger, Daniel J.
This paper describes a study demonstrating a new method of state-of-charge (SoC) estimation for batteries in real-world electric vehicle applications. This method combines realtime model identification with an adaptive neuro-fuzzy inference system (ANFIS). In the study, investigations were carried down on a small-scale battery pack. An equivalent circuit network model of the pack was developed and validated using pulse-discharge experiments. The pack was then subjected to demands representing realistic WLTP and UDDS driving cycles obtained from a model of a representative electric vehicle, scaled match the size of the battery pack. A fast system identification technique was then used to estimate battery parameter values. One of these, open circuit voltage, was selected as suitable for SoC estimation, and this was used as the input to an ANFIS system which estimated the SoC. The results were verified by comparison to a theoretical Coulomb-counting method, and the new method was judged to be effective. The case study used a small 7.2 V NiMH battery pack, but the method described is applicable to packs of any size or chemistry.
Open Access
Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies
(IET, 2016-04-21) Fotouhi, Abbas; Auger, Daniel J.; Propp, Karsten; Longo, Stefano
In this study, a framework is proposed for battery model identification to be applied in electric vehicle energy storage systems. The main advantage of the proposed approach is having capability to handle different battery chemistries. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and Lithium-Sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parametrisation is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. The use of identified parameters for battery state-of-charge (SOC) estimation is then discussed. It is demonstrated that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery’s open circuit voltage (OCV) is adequate for SOC estimation. However, Li-S battery SOC estimation can be challenging due to the chemistry’s unique features and the SOC cannot be estimated from the OCV-SOC curve alone because of its flat gradient. An observability analysis demonstrates that Li-S battery SOC is not observable using the common state-space representations in the literature. Finally, the problem’s solution is discussed using the proposed framework.
Open Access
Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies
(The Institution of Engineering and Technology, 2017-07-06) Fotouhi, Abbas; Auger, Daniel J.; Propp, Karsten; Longo, Stefano
In this study, battery model identification is performed to be applied in electric vehicle battery management systems. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and lithium-sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parameterisation is performed in both cases using the prediction-error minimisation algorithm applied to experimental data. Performance of a Li-S cell is also tested based on urban dynamometer driving schedule (UDDS) and the proposed parameter identification framework is applied in this case as well. The identification results are then validated against the exact values of the battery parameters. The use of identified parameters for battery state-of-charge (SOC) estimation is also discussed. It is shown that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery open circuit voltage (OCV) is adequate for SOC estimation whereas Li-S battery SOC estimation is more challenging due to its unique features such as flat OCV–SOC curve. An observability analysis shows that Li-S battery SOC is not fully observable and the existing methods in the literature might not be applicable for a Li-S cell. Finally, the effect of temperature on the identification results and the observability is discussed by repeating the UDDS test at 5, 10, 20, 30, 40 and 50°C.
Open Access
Electric vehicle energy consumption estimation for a fleet management system
(Taylor and Francis, 2019-10-30) Fotouhi, Abbas; Shateri, Neda; Laila, Dina Shona; Auger, Daniel J.
Accurate estimation of vehicles’ energy consumption is a demanding task. It might not be so critical for conventional vehicles because of their high travel range however, this is something important for electric vehicles (EVs). On the other hand, EVs with less energy on board, need more accurate energy management systems. This study focuses on the development of an energy consumption estimation model to be used in an EV fleet management system (FMS). The proposed estimator consists of a vehicle model, a driver model, and terrain models. It is demonstrated that a combination of these three parts can provide an accurate estimation of EV energy consumption on a particular route. As part of this study, a commercially-available passenger car is modelled using MATLAB/Simulink. A number of specific routes are selected for EV road testing to be driven for simulation model verification. In the second part of this study, the impact of energy consumption estimation accuracy is investigated at a larger scale for a fleet of EVs. It is quantitatively demonstrated how much sensitive is the performance of a FMS to the accuracy of the energy estimator. Simulation results have shown that the total energy consumption of an EV fleet is decreased significantly by improving the estimation accuracy. It is also demonstrated how the uncertainties in EV energy consumption estimation limits the overall performance of a FMS.
Open Access
Energy-optimal overtaking manoeuvres of Formula-E cars
(Taylor and Francis, 2022-07-11) Liu, Xuze; Fotouhi, Abbas; Auger, Daniel J.
Overtaking in motorsport races is a crucial action in order to gain a higher position during a race. In Formula-E, it not only requires precisely performed overtaking manoeuvres but also requires appropriate energy management which is also a crucial element in race strategy due to the limited total amount of energy on board. This study proposes the use of optimal control techniques for feasibility and energy management analysis for such a complex problem. The analysis involves two cars on a track and the goal is to find the optimal overtaking strategy under various scenarios. The results demonstrate that overtaking can be infeasible despite that there is a potential speed advantage over the target car. An overtaking manoeuvre can be executed efficiently by using higher propulsion power. When an overtaking is feasible, the energy-optimal overtaking position varies according to different initial conditions.
Open Access
Enhanced online identification of battery models exploiting data richness
(IEEE, 2023-05-11) Cai, Chengxi; Auger, Daniel J.; Perinpanayagam, Suresh
The online model parameter identification is essential to ensure the accuracy and dependability of other battery management system (BMS) tasks in the case of battery degradation and operational environment change. Traditional recursive least squares (RLS) algorithms have always been dependent on persistently exciting data, which limits their ability to operate online when this cannot be guaranteed. This paper proposed a modified RLS method that selects the data richest point for parameter identification. In this model, Fisher information matrix and Cramer-Rao bound are utilised to evaluate the data richness. The final algorithms solve the operational limitations of RLS algorithms, enabling a reliable online model parameter identification under real-world dynamic conditions. The identified model parameters from the single cycle dynamic stress test (DST) of an NCM battery are verified by terminal voltage and state of charge (SoC) estimation with the Root Mean Square Error (RMSE) 0.0332 and 0.0131, respectively.